Data Engineering · PySpark & Databricks

PySpark on Cricket Data End-to-End in Databricks

Take raw match data to real insight — DataFrames, analysis, tuning and saved outputs, all in one live build.

Most PySpark tutorials stop at .show() — interviews don't. In two hours we build a complete PySpark project on real cricket match data in Databricks: load the raw data, run advanced DataFrame analysis with groupBy, window functions, joins and UDFs, visualise top scorers, run rates and win margins, tune it with caching, partitioning and shuffle settings, then save the processed outputs to Delta. Cricket data makes it fun; the skills — analysis, tuning, persistence — are exactly what a Data Engineer gets paid for.